Why Develop Demand Planning
Factory Demand Planning
Lack of high quality business data
Single data source, fragmented management, unable to build effective data correlation
High data noise, which affects forecasting methods and results
Lack of in-depth data mining
Operation challenges
Variety of SKUs is getting larger and larger, with a large number of long-tail goods, resulting in a heavy and difficult management burden
Growing demand volatility and increased complexity due to external factors:
  • Promotions
  • Competitor promotions
  • Holidays, large events, weather, etc.
Sales opportunities go fast
Identifying missed sales opportunities due to stock out
Reducing the impact of stock-outs in demand forecasting
How to ensure proper replenishment of goods to ensure:
  • Best sellers are not restocked
  • No backlog of general merchandise inventory
Manual decision making challenges
Inability to utilize multi-dimensional data
Decision quality fluctuates greatly and is difficult to quantify
High employee turnover, experience of good employees cannot be accumulated
DP - A smarter way to forecast demand
Artificial intelligence can not only consider far more dimensions and factors than individuals and teams, but also can continuously learn and iterate
Predictive Process Model
1 Data import and governance
Intelligent identification of abnormal and characteristic data based on statistics and big data analysis technology, automatic de-duplication, rejection, interpolation and smoothing of outliers, identification of restricted sales periods.

Historical sales orders

geographic information of business areas

Product life cycle

consumer portrait

category characteristics

Weather and climate

new product launch plan

holiday calendar

promotional events

abnormal sales

2 Feature construction and slicing analysis
After inputting of the governed data, we automatically analyze which influence factors are positively associated with sales, and intelligently build feature quantities through machine learning, deep learning, neural networks and other artificial intelligence technologies.
3 Multi-level hybrid model prediction and fusion
Comprehensive use of time series, machine learning, deep learning and other frontier algorithms in various fields, iterative training sample data, for different types of goods, capable learn the most appropriate model fusion.
4 Prediction bias correction and model Performance Optimization
Combining with various built-in algorithms, the system intelligently calculates the forecast accuracy and forecast deviation rate, and obtains the model with the smallest deviation and corresponding parameter settings for a specific cycle and dimension.

Get the latest sales data

Forecast accuracy

Forecast correction

Forecast monitoring

Continuous Model Performance Optimization

Product Strengths
UHAlean's demand planning product organically blends algorithms and human experience in a process that improves forecast accuracy by leveraging the strengths of each.

Algorithm Strengths

Mining laws: Machine learning, data mining and other algorithms can effectively mine the hidden laws behind high-dimensional data and identify the combination of factors with a significant impact on sales
Automatic adaptation: automatically adapting to the sales patterns of different products, channels and time periods

Human experience advantage

Flexible response: mastering more non-standard information, such as local emergencies, etc
Manual Identification: better detection of data noise
Forecast Product Value
Value that demand predicting products and solutions can bring to customers

Forecast accuracy improvement

In comparison to traditional forecasting methods, the prediction accuracy of core products is expected to improve by 13%, and the prediction accuracy of all products is expected to improve by 11%.

Forecast labor input saving

Through the automatic parameter adjustment function of the model, the manual input of the forecasting department is reduced, and the prediction cost is significantly decreased

Forecast stability improvement

Compared with traditional manual prediction, Machine Learning algorithm forecasts provide a more stable effect

Forecast dimension customization

Time dimension can be customized to better fit the actual use case

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